Huiqiang Jiang conducts research on system-algorithm co-design for efficient LLM inference and training.
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Huiqiang Jiang as author
Minimizing training-inference discrepancy and policy staleness enables reliable token-level surrogate optimization in RL with LLMs, with importance sampling correction and routing replay being essential for stable training at scale.
“Author of "Qwen Explains Why Minimizing Training-Inference Gaps and Policy Staleness Improv" on arXiv”